MIASNet: A medical image segmentation method predicting future based on past and current cases

Shiqiang Ma, Xuejian Li, Jijun Tang, Fei Guo

Abstract: Fast and accurate segmentation of medical images is essential for the diagnosis and treatment of diseases. The automatic segmentation technology based on deep learning has achieved encouraging performance in segmentation accuracy. However, the improvement of segmentation accuracy usually requires a larger network structure, which also leads to a decrease in segmentation speed. In this study, we propose a medical image anticipation segmentation net (MIASNet), in order to further improve the segmentation speed under the premise of excellent segmentation accuracy. For 3D medical images, we use the spatial association of the previous frame and the current frame as input data to predict the segmentation results of the next frame. Our approach consists of three lightweight sub-networks, which are used to learn the mapping relationship of the spatial domain. In order to make full use of the generating ability of the deep learning network, we use group convolution to obtain diversified prediction results. On the multimodal brain tumor image segmentation (BraTS) 2020 dataset, MIASNet achieves excellent segmentation accuracy without using the target frame that need to be segmented as the network input. Therefore, our proposed segmentation network can be used in a wider range of real-time medical applications.